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Advances in Modern Age Technologies for Health and engineering Science (AMATHE), International Conference on

作者： Swastick Ganguly Jathin S M Mohak M Sanjay BM Somashekhar N Shobharani Department of Computer Science Amrita School of Computing Mysuru Campus Dept of Computer Science and Engineering Gitam School of Technology Gitam University Bengaluru Campus

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Cotton plays a vital role in agricultural sustainability, extending beyond its textile applications. Pathogens pose significant threats to cotton plants and farmers' livelihoods, highlighting the critical need for disease detection to maintain crop health and ensure optimal yields. Our research pioneers a novel approach to disease detection in cotton through advanced image analysis techniques. This involves exploring deep learning methodologies specifically tailored for detecting cotton leaf diseases and enhancing accuracy by integrating CNN models, while balancing labor intensity and cost considerations. Leveraging the Convolutional Neural Networks (CNN), the combination of the interpretability of Densenet 121 with the stability of ResNet 50, assessing the strength of their combined algorithm. This hybrid model accurately classifies various cotton plant diseases, enabling fine-grained analysis. With a precision-driven approach, the proposed model achieves exceptional results, accurately identifying six distinct diseases, including fungal, bacterial, and viral pathogens, with an impressive 96% accuracy rate. The research aims to provide a comprehensive and dependable solution for disease management in the cotton industry, ultimately promoting crop health, productivity, and sustainability

关键词： Deep learning Training Plant diseases Pathogens Accuracy Computational modeling Crops

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Multi Cancer Prediction using Deep Learning and CNN Algorithm

Multi Cancer Prediction using Deep Learning and CNN Algorith...

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International Conference on Inventive computing and Informatics (ICICI)

作者： Siddharth Patel Zayd Hassan S. Iniyan Usha Desai Department of Computer Science and Engineering SRM Institute of Science and Technology Kaatankulathur Chennai Tamilnadu India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kaatankulathur Chennai Tamilnadu India S. E. A. College of Engineering & Technology Bengaluru

ISBN: (数字)9798350373295

ISBN: (纸本)9798350373301

Deep learning approach is used to predict the chance of acquiring several types of malignancies, including breast, brain, lung, colon, oral, kidney, and cervical cancer. To find patterns and risk factors linked to each kind of cancer, deep learning algorithms, such as convolutional neural networks and recurrent neural networks, are trained on enormous databases of patient data, including genetic markers, lifestyle variables, and medical history. Deep learning algorithms can accurately forecast an individual’s risk of acquiring cervical, lung, colon, oral, kidney, breast, or brain cancer by evaluating these intricate datasets. Results indicate varied performance across cancer types, with cervical, breast, lung, and colon cancer models exhibiting high accuracy (>98%) and lower validation losses, suggesting strong generalization capabilities. Conversely, kidney and oral cancer models displayed signs of overfitting, emphasizing the challenges of model generalization. These findings highlights the DL’s potential in enhancing cancer detection, early diagnosis, and personalized treatment planning.

关键词： Deep learning Prevention and mitigation Lung Breast Prediction algorithms Brain modeling History

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Optimization of Lightning Protection System Using Multi-Objective Optimization Techniques 37

Optimization of Lightning Protection System Using Multi-Obje...

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37th International Conference on Lightning Protection, ICLP 2024

作者： Tang, Jia-Wen Wooi, Chin-Leong Tan, Wen-Shan Cooray, Vernon Kadir, Mohd Zainal Abidin Ab Afrouzi, Hadi Nabipour Faculty of Electrical Engineering Technology Universiti Malaysia Perlis Pauh Putra Campus Perlis Arau02600 Malaysia Faculty of Electrical Engineering Technology Universiti Malaysia Perlis Pauh Putra Campus Perlis Arau02600 Malaysia School of Engineering and Advance Engineering Platform Monash University Malaysia Jalan Lagoon Selatan Selangor Bandar Sunway47500 Malaysia Department of Electrical Engineerin Uppsala University Uppsala Sweden Faculty of Engineering Universiti Putra Malaysia Selangor Seri Kembangan Malaysia Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Sarawak Kuching93350 Malaysia

ISBN: (纸本)9783800764174

This study aims to enhance the reliability of an 80-line distribution system against lightning strikes by minimizing interruptions and associated costs. ATPDraw transient models simulate Lightning Protection System (LPS) configurations such as feeder shielding and grounding, to obtain critical lightning currents for estimating fault rates due to lightning strikes. Employing and comparing the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) with the Multi-Objective Particle Swarm Optimization (MOPSO), the optimization model integrates diverse LPS configurations into the distribution network. The optimal LPS configurations are determined with the minimum System Average Interruption Frequency Index (SAIFI), the Momentary Average Interruption Frequency Index (MAIFI) and associated costs. The study identifies specific grounding rod configurations, reducing SAIFI from 4.52 int./yr. to 3.47 int./yr. and MAIFI from 22.58 int./yr. to 17.33 int./yr. Validation under various cost constraints reveals a direct relationship between investment and system reliability. NSGA-II proves more effective in optimizing grounding systems and shielding measures compared to MOPSO, highlighting the significance of optimized lightning protection strategies for system resilience, cost-effectiveness, and regulatory compliance in power distribution networks. © 2024 IEEE, © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Multiobjective optimization

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Dynamic Surface-enabled Air Quality Evaluation

Dynamic Surface-enabled Air Quality Evaluation

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International Conference on Contemporary computing and Informatics (IC3I)

作者： M Gurupriya Rahul B Karthikeyan Department of CSE Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru Karnataka India School of Electronics Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The current generation of 5G technology has reached its limitations in terms of both frequency and wavelength. There is no room for further increasing the frequency, nor can we decrease the wavelength any further. In light of this challenge, alternative solutions for signal transmission must be explored. One such promising avenue is the utilization of Reconfigurable Intelligent Surfaces (RIS). RIS refers to a meta surface with the capability to dynamically alter its impedance, enabling efficient manipulation of signal reflection and steering. It holds significant potential for advancing beyond 5G towards the envisioned 6G era. This paper endeavours to delve into the behaviour of RIS through modelling, with a specific focus on its application in Remote Air Quality Monitoring.

关键词： Surface impedance 5G mobile communication Surface waves Computational modeling Reconfigurable intelligent surfaces Air quality Reflection Impedance Informatics Monitoring

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DPS: Design Pattern Summarisation Using Code Features

arXiv

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arXiv 2025年

作者： Nazar, Najam Sikka, Sameer Treude, Christoph School of Computer and Mathematical Sciences The University of Adelaide Australia School of IT and Engineering Ada University Azerbaijan Faculty of Engineering and Information Technology The University of Melbourne Australia School of Computing and Information Systems Singapore Management University Singapore

Automatic summarisation has been used efficiently in recent years to condense texts, conversations, audio, code, and various other artefacts. A range of methods, from simple template-based summaries to complex machine learning techniques—and more recently, large language models—have been employed to generate these summaries. Summarising software design patterns is important because it helps developers quickly understand and reuse complex design concepts, thereby improving software maintainability and development efficiency. However, the generation of summaries for software design patterns has not yet been explored. Our approach utilises code features and JavaParser to parse the code and create a JSON representation. Using an NLG library on this JSON representation, we convert it into natural language text that acts as a summary of the code, capturing the contextual information of the design pattern. Our empirical results indicate that the summaries generated by our approach capture the context in which patterns are applied in the codebase. Statistical evaluations demonstrate that our summaries closely align with human-written summaries, as evident from high values in the ROUGE-L, BLEU-4, NIST, and FrugalScore metrics. A follow-up survey further shows that DPS summaries were rated as capturing context better than human-generated summaries. Copyright © 2025, The Authors. All rights reserved.

关键词： Software design

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Clause-level Relationship-aware Math Word Problems Solver

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Machine Intelligence Research 2022年第5期19卷 425-438页

作者： Chang-Yang Wu Xin Lin Zhen-Ya Huang Yu Yin Jia-Yu Liu Qi Liu Gang Zhou Anhui Province Key Laboratory of Big Data Analysis and Application School of Data ScienceUniversity of Science and Technology of ChinaHefei 230026China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Laboratory of Mathematical Engineering and Advanced Computing Information Engineering UniversityZhengzhou 450001China

Automatically solving math word problems,which involves comprehension,cognition,and reasoning,is a crucial issue in artificial intelligence *** math word problem solvers mainly work on word-level relationship extraction and the generation of expression solutions while lacking consideration of the clause-level *** this end,inspired by the theory of two levels of process in comprehension,we propose a novel clause-level relationship-aware math solver(CLRSolver)to mimic the process of human comprehension from lower level to higher ***,in the lower-level processes,we split problems into clauses according to their natural division and learn their *** the higher-level processes,following human′s multi-view understanding of clause-level relationships,we first apply a CNN-based module to learn the dependency relationships between clauses from word relevance in a local ***,we propose two novel relationship-aware mechanisms to learn dependency relationships from the clause semantics in a global ***,we enhance the representation of clauses based on the learned clause-level dependency *** expression generation,we develop a tree-based decoder to generate the mathematical *** conduct extensive experiments on two datasets,where the results demonstrate the superiority of our framework.

关键词： Artificial intelligence(AI) artificial neural network(ANN) computational mathematics machine intelligence machine learning

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CVchain: A Cross-Voting-Based Low Latency Parallel Chain System 23

CVchain: A Cross-Voting-Based Low Latency Parallel Chain Sys...

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23rd IEEE International Conference on Trust, Security and Privacy in computing and Communications, TrustCom 2024

作者： Wang, Jianrong Ren, Yacong Hu, Dengcheng Li, Qi Li, Sen Li, Xuewei Liu, Xiulong Tianjin University College of Intelligence and Computing China Tianjin University School of Electrical and Information Engineering China Tianjin University School of Future Technology China

Despite existing parallel chain systems improving blockchain throughput by allowing concurrent blocks to be appended, challenges such as the excessive number of waiting blocks before confirmation and the inconsistency between block generation order and global confirmation sequence still persist. To address these challenges, we propose CVchain, a parallel chain system with a cross-voting mechanism. Blocks from other subchains are incorporated into the consistency determination of the main chain, reducing the probability of confirmation errors. Our global sorting mechanism leverages both real-time height information and the implicit temporal order contained in voting to improve the accuracy of block ordering. Furthermore, our voting mechanism randomly splits the mining power of the system, preventing targeted attacks on the specific subchain and defending against liveness attacks. We prove the safety and liveness properties of CVchain. We demonstrated its performance with a prototype implementation and large-scale experiments involving 200 nodes across 10 cloud servers in a distributed network environment. The results indicate that CVchain achieves a latency reduction of approximately 32.3% at a confirmation error probability of 0.01 while maintaining throughput levels comparable to OHIE. Additionally, it provides enhanced transaction ordering services. © 2024 IEEE.

关键词： Multiprocessing systems

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IoT-Enabled Deep Learning Framework for Smart Infant Health Monitoring

IoT-Enabled Deep Learning Framework for Smart Infant Health ...

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International Conference on ent computing and Control Systems (ICICCS)

作者： Madhu R U.V. Anbazhagu R. Santhana Krishnan Kruthika Paulraj Ezhil E. Nithila R. Umesh Department of Computer Science and Engineering Ramco Institute of Technology Rajapalayam India Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of Information Technology Sona College of Technology Salem India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

The Intelligent Baby Monitoring System integrates AI-driven cry detection, sleep tracking, and environmental safety analysis to enhance infant care. The system employs CNN-BiLSTM and Wav2Vec2.0 for cry classification, distinguishing between hunger, pain, and sleepy cries based on pitch, energy, and duration. Sleep monitoring utilizes YOLOv8, OpenPose, and LSTM to analyze sleep stages, detecting awake, asleep, restless, and distress states through body movement intensity. Anomaly detection is performed using an LSTM Autoencoder, identifying abnormal gas levels (MQ-135), temperature and humidity fluctuations (DHT22), and deviations in heart rate and SpO 2 levels (MAX30102). Heart rate monitoring leverages 1D-CNN and LSTM, classifying conditions such as bradycardia (<100 bpm) and tachycardia (>180 bpm), while oxygen saturation levels below 90% indicate hypoxia. Cradle safety monitoring employs YOLOv8 and CNN-LSTM, detecting movement patterns such as excessive shaking and escape attempts, with acceleration thresholds exceeding 0.7g triggering alerts. The system is implemented on an NVIDIA Jetson Nano, ensuring real-time inference with optimized TensorRT models. Data transmission to AWS IoT Core enables cloud-based storage and remote monitoring through a mobile application. By integrating deep learning, edge AI, and cloud computing, this system offers an efficient, intelligent, and real-time solution for infant monitoring, ensuring timely alerts and enhanced caregiver response. The proposed system is evaluated across multiple performance metrics, demonstrating high accuracy in cry classification, sleep tracking, and anomaly detection, making it a robust tool for infant safety and well-being.

关键词： Deep learning Pediatrics Cloud computing Accuracy Edge AI Real-time systems Safety Monitoring Anomaly detection Long short term memory

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New Entanglement-Assisted Concatenated Quantum Codes Exceed Quantum Gilbert-Varshamov Bound 10

New Entanglement-Assisted Concatenated Quantum Codes Exceed ...

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10th International Conference on Computer and Communications, ICCC 2024

作者： Wang, Renxu Fan, Jihao School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Laboratory for Advanced Computing and Intelligence Engineering Wuxi214083 China

Quantum error-correction codes leverage encoding and redundancy to effectively detect and correct errors, thereby enhancing the reliability and accuracy of signal detection. Based on the scheme of entanglement-assisted concatenated quantum codes (EACQCs), we construct both new binary and non-binary EACQCs. Several of the new EACQCs achieve better parameters than the best-known standard entanglement-assisted quantum error-correction codes while maintaining the same code lengths and net transmissions. The quantum Gilbert-Varshamov (GV) bound serves as a crucial benchmark for evaluating quantum code parameters. As the code length increases, we construct binary and non-binary EACQCs that exceed the quantum GV bound. Index Terms-entanglement-assisted quantum error-correction code, entanglement-assisted concatenated quantum codes, quan-tum Gilbert-Varshamov bound © 2024 IEEE.

关键词： Quantum entanglement

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Recommendation System using Inference-based Graph Learning - Modeling and Analysis 2

Recommendation System using Inference-based Graph Learning -...

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2nd International Conference on Innovative Sustainable Computational Technologies, CISCT 2022

作者： Bhaskaran, S. Hariharan, S. Veeramanickam, M.R.M. Bharathiraja, N. Pradeepa, K. Marappan, Raja Sastra Deemed University School of Computing Thanjavur India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India St. Michael College of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9781665474160

Recently, the development of recommendation systems has been an emerging area where the target is the learner's preferences, and styles should be learned from past interactions to predict the ratings and recommendations of the new items. The significant critiques and challenges are some of the datasets are sparse and the bias in the ratings and popularity of the items that conventional recommenders can't efficiently process. This research constructs the graph model to merge the items and user interaction to enhance user representations. The new inference operator is designed to remove the bias factors and to draw inferences. The users and items are represented with high quality by constructing the semantic space that connects the users and the item's characteristics. The proposed model is simulated on real-world datasets, proving that the model produces significant outcomes better than existing methods. © 2022 IEEE.

关键词： Recommender systems

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